2. Data and Methods
Deep SOLO floats measure pressure and time during descent nominally at 5
to 50 dbar intervals, depending on how engineering parameters are set
for an individual float. These measurements are telemetered to shore as
part of the engineering data when the float surfaces during each
nominally 10-day cycle, and reported in Argo trajectory (.traj) files.
We only use these data from profiles that sample to at least 2995 dbar,
and limit our analysis of the data to pressures greater than 800 dbar,
below the main pycnocline in the regions analyzed. We also remove three
profiles with vertical gaps in these engineering data that are too long
to smooth over. The screening results in 10070 profiles in total, mostly
in the Brazil Basin of the western South Atlantic and the Southwest
Pacific Basin, but also in the South Australian Basin, the
Australian-Antarctic Basin, the North American Basin (western north
Atlantic), and a few in the Central and Northeast Pacific basins (Figure
1).
For each profile extending to at least 2995 dbar we estimate vertical
velocities from first differences of pressures divided by those of
times. We discard the three deepest estimates to avoid bottom
interactions, then apply a loess smoother (Cleveland & Devlin, 1988)
with a half-power point of ~150 dbar to estimate
vertical velocities on a regular grid at 10-dbar intervals from 1000
dbar to the deepest pressure (rounded down to the closest 10 dbar value
shallower than that pressure). We then fit a second-order polynomial to
these gridded vertical velocities as an estimate of the background fall
rate that steadily decreases with increasing pressure, and subtract
those background fall rates from each profile to yield the residual
vertical velocities (hereafter w’ ) relative to the background
fall rates. Typical background fall rates are about 0.17 (±0.01) dbar
s-1 at 1000 dbar, and decrease nearly linearly to
about 0.07 (±0.01) dbar s-1 at profile maximum
pressures, where the uncertainties (here and throughout the manuscript)
are one standard deviation.
We calculate the mean deep w’ variances averaged from 1000 dbar
to the maximum pressure analyzed for each profile (Figure 2a). We also
apply a Morlet wavelet transform (Torrence & Compo, 1998) to each
profile to estimate the dominant vertical wavelength of w’(Figure 2b). We define this dominant vertical wavelength as the location
of the maximum value of the wavelet power spectrum of w’ for all
vertical wavelengths and pressures not influenced by zero-padding edge
effects (i.e., heeding the “cone of influence”).
We look at the correlation of the mean deep w’ variances and
dominant vertical wavelengths with local bottom topographic roughness.
To do that we compute the variance of ocean bathymetry (GEBCO
Compilation Group, 2021) at 1’ resolution within 40-km square bins on a
0.25° x 0.25° grid and use the resulting map to interpolate roughness to
float locations.